To develop and evaluate the components of a modelling system to forecast floods in rivers draining steep mountainous catchments into flat plains where they flood large urban areas. Traditional flood warning systems are inadequate because of the fast response time of the catchments. Accurate forecasts of precipitation are essential for successful flood warnings. The proposed system will consist of a high-resolution limited area meteorological model together with a hydrological catchment model and an hydraulic channel network model. All of these models exist separately and are available to the proposers. However the refinement and integration of all of them into a flood forecasting system has not yet been done for steep mountainous catchments.
To investigate the factors affecting the accuracy and reliability of flooding forecasts from such a system and to determine the nature of the trade-off between forecast lead-time, spatial resolution and forecast accuracy.
The project will:
develop adequate parametrisation schemes in limited area high resolution atmosphere models for clouds and
precipitation, which are as independent as possible from grid resolution.
investigate the sensitivity of the resulting high resolution limited area meteorological models to (i) orography
steepness, and (ii) the formulation of the horizontal diffusion.
determine the spatial resolution required in a meteorological model for precipitation forecasts accuracy of the
determine the most appropriate type and complexity of catchment model and channel network model required
for (i) adequate discharge forecasts and (ii) for adequate flood warnings?
establish the effect of backwater from a tidal estuary on water levels where appropriate.
choose the most appropriate algorithm for triggering high resolution modelling?
The work will progress European modelling and forecasting abilities to the point where floods in steep
catchments can be forecast and will quantify the performance to be expected from these methods.
Funding SchemeCSC - Cost-sharing contracts